npx skills add a5c-ai/babysitter --skill monte-carlo-simulation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Monte Carlo Simulation ## Purpose Provides Monte Carlo methods for uncertainty quantification, integration, and probabilistic analysis. ## Capabilities - Standard Monte Carlo sampling - Importance sampling - Stratified sampling - Quasi-Monte Carlo (Sobol, Halton sequences) - Markov chain Monte Carlo - Convergence analysis ## Usage Guidelines 1. **Sampling Strategy**: Choose appropriate sampling method 2. **Sample Size**: Determine sufficient sample sizes 3. **Variance Reduction**: Apply variance reduction techniques 4. **Convergence**: Monitor convergence diagnostics ## Tools/Libraries - NumPy - scipy.stats - SALib
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the monte-carlo-simulation skill do?
Monte Carlo methods for uncertainty quantification
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill monte-carlo-simulation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.